title: Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers.
abstract: Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations.Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations.
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, NY 10065, USA. reva@cbio.mskcc.org.
authors.forename: B
authors.initials: B
authors.lastname: Reva
mesh_terms: D019295:Computational Biology / Q000379:methods*; D019277:Entropy; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D058977:Molecular Sequence Annotation / Q000379:methods; D020125:Mutation, Missense / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics*; D011487:Protein Conformation; D012641:Selection, Genetic